Over the course of the past decade, a variety of neuroimaging technologies allow the structure and function of the intact brain to be studied. This presents a tremendous opportunity to understand the human brain. Neuroimaging has the potential to reveal some of nature's most closely held and significant secrets, and informatics can assist in realizing this potential.
As defined in Neuroimaging Informatics Technology Initiative (NifTI) 2000 Workshop, “informatics, a computerized way to handle data, is used to design and implement the manner in which the imaging instruments capture signals generated by the brain, as well as the behavioral tasks used to probe particular brain systems, reconstruct the resulting signals, statistically analyze the data, and visualize the results”. By this definition, it is easy to find out that the informatics includes a variety of technologies, e.g., collection of data using new acquisition techniques (e.g. phase array coil), statistical analysis and processing of data, and visualization of data. However, the great potential of informatics in neuroimaging has been impeded by inadequate coordination regarding the development and distribution of the informatics tools needed to meet this challenge. Existing informatics tools have been developed separately, and now widely used software products are not adequately optimized for meeting the variety of needs of the broader community.
There exist a strong need that the conclusions drawn from fMRI studies regarding the relationship between functional activation signals and function of the brain should be based upon a clear understanding of the manner in which various tools affect the data at each stage of processing. Knowing that the operations performed by informatics tools are valid is important for interpreting the results of fMRI studies. In addition, comparing different tools to identify the conditions under which, and uses to which, they are best suited is significant information to both the researchers and clinicians. In this background section, some recent studies of fMRI activation simulation, statistical analysis tools, and parallel MRI reconstruction using phased array coils will be briefly reviewed.
Simulation of fMRI Activation and BOLD Signals
After conception and implementation of any new neuro-imaging informatics tools, validation is an important step to ensure that the procedure fulfills all requirements set forth at the initial design stage. Manufacturer's physical phantoms were first used for this purpose. However, these are typically just static phantoms filled with water or gel. The informatics tools must be evaluated using a comprehensive validation that requires additional use of simulated data since it is very difficult to establish ground truth with in vivo data. Experiments with simulated data permit controlled evaluation over a wide range of conditions, such as different Signal-to-Noise Ratio (SNR), contrast-to-noise ratio, and signal intensities. Such considerations have become increasingly important with the rapid growth of neuro-imaging.
Computer simulations of the Blood Oxygenation Level Dependent (BOLD) signals and neural activation have been developed by many researchers. In a recent publication (Sorenson, J. A. & Wang, X, ROC methods for evaluation of fMRI techniques. Magn. Reson. Med., 36:737–744 (1996)), the time course data was represented by a zero-baseline boxcar function. Gaussian noise was added to simulate physiologic “noise”, after which the data were convolved with a Poisson function to simulate hemodynamics smoothing and delay. Also, structured noise in the form of slopes to simulate signal drift and sinusoidal oscillations to simulate respiratory motion were added. These simulated fMRI responses were finally added to null data sets acquired on normal subjects, denoted as “human data”. In one study (Constable, R. T. et al. Quantifying and comparing region-of-interest activation patterns in functional brain MR imaging: methodology considerations, Magnetic Resonance Imaging, 16(3):289–300 (1998)), Gaussian-shaped activation signals were added with Gaussian distribution of white noise.
Another study (Morgan, V. L. et al. Comparison of functional MRI image realignment tools using a computer-generated phantom. Magn Reson Med. 2001 September; 46(3):510–4) discussed the development of a computer-generated phantom to compare the effects of image realignment programs on functional MRI (fMRI) pixel activation. The phantom is a whole-head MRI volume with added random noise, activation, and motion. It allows simulation of realistic head motions with controlled areas of activation. Without motion, the phantom shows the effects of realignment on motion-free data sets. Prior to realignment, the phantom illustrates some activation corruption due to motion. A recent paper (Desco, et al. Multiresolution analysis in fMRI: sensitivity and specificity in the detection of brain activation. Hum Brain Mapp. 2001 September; 14(1):16–27) presents a study that was undertaken to assess the performance of different wavelet decomposition schemes by making use of a “gold standard,” a computer-simulated phantom. As activation areas are then known “a priori,” assessments of sensitivity, specificity, ROC curve area, and spatial resolution can be obtained. This approach has allowed the study of the effect of different factors: the size of the activation area, activity level, signal-to-noise ratio (SNR), use of pre-smoothing, wavelet base function and order and resolution level depth. Recently, a Functional Data Simulator has been provided by SENSOR SYSTEMS (Medical Numerics, Inc., Sterling, Virginia) in their software package MEDx. This functional data simulator (MEDx) is a graphical tool for constructing curves of signal intensity vs. time at a single pixel and then extrapolating to volumetric data. It can also be used to create synthetic fMRI data for similar exploration of the effects of noise and timing.
However, these studies, while providing a simulated activation signal, do not account for variations in the activation levels of different pixels and are unable to depict accurately the temporal response of the activation associated signal change. And the most important issue is that these simulations do not account for the real MR imaging environment (non-stationary rather than stationary, or dynamic rather than static), spin characteristics and the noises introduced during the whole imaging process, i.e. noises from MR power system, pre-amplifier, transmit/receive, and digitization (A/D) error.
By reviewing the literature, one can see that there are very limited measurements, such as the signal-to-noise ratio or contrast-to-noise ratio, where static phantom (a simple container full of liquid) has usually been used that provides static MR signals instead of dynamic signal changes. As for the fMRI research, systematic comparison of the analysis methods presented in fMRI cannot be conclusive if assessment is based only on the highly variable activity of the human brain. Calibrated, repeatable fMRI signals are needed for a reliable validation.
Statistical Analysis of Activation
In recent years, a variety of advanced high performance informatics tools have emerged for statistical pattern mapping of functional data and fMRI data visualization. Among them are the general-purpose fMRI analysis packages, such as AFNI, Brain Voyager™, BrainTools, FIASCO, fmristat, iBrain, Lyngby, MEDx, scanSTAT, and Statistical Parametric Mapping (SPM) as well as Special-purpose software packages, such as AIR, ANALYZE, ANIMAL, FreeSurfer, and SureFit. Qualitative characterizations and comparisons have been done (Gold et al. Functional MRI statistical software packages: a comparative analysis. Hum Brain Mapp. 1998;6(2):73–84, Lange et al. Staatistical procedure for fMRI. Chapter 27 in Functional MRI, C. T. W. Moonen & P. A. Bandettini (Eds), Springer-Verlag (1999)). However, due to lack of a benchmark database, quantitative validation and comparisons of these tools are difficult and sometimes impossible.
The field of fMRI has been dominated by the notion of detectability of activation in a noisy background, and tremendous effort has been placed in the area of statistical signal detection methods. Among these statistical methods, some provide maps or images of descriptive statistics, such as z-score, statistical t-test, General Linear Model (GLM), and Kolmogorov-Smirnov test. Generally speaking, all such maps represent a ratio of the activation magnitude to the measured signal variation.
Based on the differences in model, these techniques can be divided into two groups. The first is the parametric group. In general, parametric approaches depend on assumptions about the possible parametric families of distributions generating the fMRI data. They attempt to determine the value of a parameter or parameter vector that identifies a particular member among a large parametric family of plausible probability distributions that generate the fMRI data. The parametric group includes for example the statistical t-test, cross-correlation, and general linear model (Friston et al. Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping, 2:189–210 (1995), Worsley & Friston Analysis of fRMI time-series revisited-again. NeuroImage, 2:173–181 (1995)), uni-variate time series models, analysis in frequency domain, and longitudinal data analysis.
The second group is the non-parametric approaches, which make no assumptions about the possible parametric families of distributions generating the fMRI data and are thus less dependent on a specific statistical model. While this seems attractive, there is another side. If parametric assumptions are roughly correct, then a parametric, model-based approach is superior to one that does not employ these assumptions. However, the difficulty is that it is usually difficult to know the properties of the true data-generating mechanism, and some statistical compromise between knowns and unknowns is often a pragmatic course of action. The non-parametric statistical procedures include, for example, the Kolmogorov-Smirnov (KS) test, probabilistic analysis (Frank et al., Probabilistic analysis of fMRI data, Magnetic resonance in medicine, 39:132–148 (1998)), information-theoretic methods (Tsai et al., Analysis of functional MRI data using mutual information. Second International Conference of Medical Image Computing and Computer-assisted Intervention, 1679:473–480 (1999); Zhao, et al. 2002 Zhao, Q., Principe, J. C., Fitzsimmons, J., Bradley, M. M., Lang, P. J. (2001), “functional Magnetic Resonance Imaging Data Analysis with Information theoretic Approaches”, “Biocomputing”, edited by Panos Pardalos and Jose Principe, Chapter 9, pp. 159–174, Kluwer, 2002, and some modern computational non-parametric statistics (e.g., permutation, jackknife and bootstrap procedure).
However, there is very limited published information about how various tools affect the data at each stage of processing, what operations performed by informatics tools are valid, and under what conditions they are best utilized. A standard procedure or ground truth is needed for an objective characterization and comparison.
Establishing Ground Truth
Informatics tools are important both to performing neuro-imaging studies, and to understanding the results. After conception and implementation of fMRI informatics tools, validation is an important step to ensure that the procedure fulfills all requirements set forth at the initial design stage. Though these tools must be evaluated on real data, a comprehensive validation should ideally involve the additional use of simulated data with known parameters, since it is very difficult to establish ground truth with in vivo data. Qualitative analyses of various informatics tools have been done using computer-simulated functional activation signals. However, these computer-simulated data are usually simply overlapped on top of anatomic images, they cannot reflect the real MR imaging process, such as the MR spin characteristics and noises introduced during the process, i.e. noise from MR power system, pre-amplifier, transmit/receive, and digitization (A/D) error. Besides, due to lack of a highly characterized data set available to the research community, it is difficult to make an objective comparison of the fMRI informatics tools. A quantitative measurement tool is needed to provide data that presents the MR imaging process, the ground truth, in order to characterize and compare informatics tools.
Defining the Effect of Statistical Analysis Tools
Informatics tools are key to all stages of fMRI. However, there is very limited published information about several fundamental aspects of informatics tools. These issues include a clear understanding of the manner in which various tools affect the data at each stage of processing, the knowledge of under what conditions operations performed by informatics tools are valid, and comparisons of different tools to identify the conditions under which they are best utilized.
Calibration of Different fMRI Experiments
Currently, numerous fMRI exams are conducted on various MRI scanners (e.g., General Electric Medical Systems, Siemens Medical Solutions, Philips Medical Systems) at different field strength (e.g., 1.5 T and 3.0 T) and with different pulse sequences (e.g., EPI, Spiral), head coils (e.g., birdcage, phased array) or protocols. With so many variables, comparison of the results become extremely difficult, making it uncomfortable to share the findings from different research groups. Therefore, a calibration tool which acts as a reference or benchmark of the specific exam is highly needed to provide useful information for comparison of the results.
Accordingly, considering all these problems, there is a need for a physical fMRI simulation device or phantom. There is a need for a device which can provide variable contrast of signals, with intensity changes following the BOLD signals, and can allow the optimization of imaging protocols. Also, there is a need for a device that can produce signals, such that the acquired signals from such a device can provide a gold standard for characterization, validation and comparison of various informatics tools. Furthermore, there is a need for a physical phantom which can provide means for instantly checking system performance. In addition, there is a need for a device or phantom that can provide one or more of the following features: a complete MR system and MR platform independent; have complete control over timing or delays, signal intensity level, motion, and physiological “noise” such as cardiac and respiratory signals; provide self-testing for quality control; and be easy to use and transportable.